Clinical Characteristics and Outcomes of COVID-19 in a low-prevalence, well-resourced setting, Sydney, Australia

Demographics, comorbidities and risk factors for severe disease from the early SARS-CoV-2 infection cases in Queensland, Australia

BACKGROUND

Demographics and comorbidities associated with coronavirus disease 2019 (COVID-19) severity differs between subpopulations and should be determined to aid future pandemic planning and preparedness.

AIM

To describe the demographics and comorbidities of patients diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Queensland (QLD), Australia, between January 2020 and May 2021. Also, to determine the relationship between these characteristics and disease severity based on the highest level of care.

METHODS

A retrospective case series analysis was conducted using data obtained from the Notifiable Conditions System. Data on patients confirmed with SARS-CoV-2 infection in QLD were included in this analysis. Descriptive statistics and logistic regression modelling were used to analyse factors that contributed to disease severity.

RESULTS

One thousand six hundred twenty-five patients with SARS-CoV-2 infection were diagnosed in the study period and analysed. The median age was 41 years and 54.3% (n = 882) were males. A total of 550 patients were hospitalised and 20 patients were admitted to the intensive care unit (ICU). In those admitted to the ICU, 95% (n = 19) were older than 45 years and 95% (n = 19) were male. Comorbidities significantly associated with hospitalisation were chronic cardiac disease (excluding hypertension) and diabetes, and for ICU admission were morbid obesity, chronic respiratory disease and chronic cardiac disease. No demographic factors were shown to be significantly associated with disease severity.

CONCLUSIONS

Comorbidities associated with the highest level of COVID-19 disease severity were morbid obesity, chronic respiratory disease and cardiac disease. These data can assist with identifying high-risk patients susceptible to severe COVID-19 and can be used to facilitate preparations for future pandemics.

Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis

BACKGROUND

After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern.

METHODS

We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2.

RESULTS

Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13-4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71-3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48-3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72-8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities (I2 > 80%; I2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics).

CONCLUSIONS

Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves.

Diabetes IN hospital - Glucose and Outcomes in the COVID-19 pandemic (DINGO COVID-19): the 2020 Melbourne hospital experience prior to novel variants and vaccinations

BACKGROUND AND AIMS

A relationship between diabetes, glucose and COVID-19 outcomes has been reported in international cohorts. This study aimed to assess the relationship between diabetes, hyperglycaemia and patient outcomes in those hospitalised with COVID-19 during the first year of the Victorian pandemic prior to novel variants and vaccinations.

DESIGN, SETTING

Retrospective cohort study from March to November 2020 across five public health services in Melbourne, Australia.

PARTICIPANTS

All consecutive adult patients admitted to acute wards of participating institutions during the study period with a diagnosis of COVID-19, comprising a large proportion of patients from residential care facilities and following dexamethasone becoming standard-of-care. Admissions in patients without known diabetes and without inpatient glucose testing were excluded.

RESULTS

The DINGO COVID-19 cohort comprised 840 admissions. In 438 admissions (52%), there was no known diabetes or in-hospital hyperglycaemia, in 298 (35%) patients had known diabetes, and in 104 (12%) patients had hyperglycaemia without known diabetes. ICU admission was more common in those with diabetes (20%) and hyperglycaemia without diabetes (49%) than those with neither (11%, P < 0.001 for all comparisons). Mortality was higher in those with diabetes (24%) than those without diabetes or hyperglycaemia (16%, P = 0.02) but no difference between those with in-hospital hyperglycaemia and either of the other groups. On multivariable analysis, hyperglycaemia was associated with increased ICU admission (adjusted odds ratio (aOR) 6.7, 95% confidence interval (95% CI) 4.0-12, P < 0.001) and longer length of stay (aOR 173, 95% CI 11-2793, P < 0.001), while diabetes was associated with reduced ICU admission (aOR 0.55, 95% CI 0.33-0.94, P = 0.03). Neither diabetes nor hyperglycaemia was independently associated with in-hospital mortality.

CONCLUSIONS

During the first year of the COVID-19 pandemic, in-hospital hyperglycaemia and known diabetes were not associated with in-hospital mortality, contrasting with published international experiences. This likely mainly relates to hyperglycaemia indicating receipt of mortality-reducing dexamethasone therapy. These differences in published experiences underscore the importance of understanding population and clinical treatment factors affecting glycaemia and COVID-19 morbidity within both local and global contexts.

Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis

IMPORTANCE

Several studies were conducted to estimate the average incubation period of COVID-19; however, the incubation period of COVID-19 caused by different SARS-CoV-2 variants is not well described.

OBJECTIVE

To systematically assess the incubation period of COVID-19 and the incubation periods of COVID-19 caused by different SARS-CoV-2 variants in published studies.

DATA SOURCES

PubMed, EMBASE, and ScienceDirect were searched between December 1, 2019, and February 10, 2022.

STUDY SELECTION

Original studies of the incubation period of COVID-19, defined as the time from infection to the onset of signs and symptoms.

DATA EXTRACTION AND SYNTHESIS

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline, 3 reviewers independently extracted the data from the eligible studies in March 2022. The parameters, or sufficient information to facilitate calculation of those values, were derived from random-effects meta-analysis.

MAIN OUTCOMES AND MEASURES

The mean estimate of the incubation period and different SARS-CoV-2 strains.

RESULTS

A total of 142 studies with 8112 patients were included. The pooled incubation period was 6.57 days (95% CI, 6.26-6.88) and ranged from 1.80 to 18.87 days. The incubation period of COVID-19 caused by the Alpha, Beta, Delta, and Omicron variants were reported in 1 study (with 6374 patients), 1 study (10 patients), 6 studies (2368 patients) and 5 studies (829 patients), respectively. The mean incubation period of COVID-19 was 5.00 days (95% CI, 4.94-5.06 days) for cases caused by the Alpha variant, 4.50 days (95% CI, 1.83-7.17 days) for the Beta variant, 4.41 days (95% CI, 3.76-5.05 days) for the Delta variant, and 3.42 days (95% CI, 2.88-3.96 days) for the Omicron variant. The mean incubation was 7.43 days (95% CI, 5.75-9.11 days) among older patients (ie, aged over 60 years old), 8.82 days (95% CI, 8.19-9.45 days) among infected children (ages 18 years or younger), 6.99 days (95% CI, 6.07-7.92 days) among patients with nonsevere illness, and 6.69 days (95% CI, 4.53-8.85 days) among patients with severe illness.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that SARS-CoV-2 has evolved and mutated continuously throughout the COVID-19 pandemic, producing variants with different enhanced transmission and virulence. Identifying the incubation period of different variants is a key factor in determining the isolation period.